Share Email Print

Proceedings Paper

Analysis of mammogram images based on texture features of curvelet sub-bands
Author(s): Syed Jamal Safdar Gardezi; Ibrahima Faye; Mohamed Meselhy Eltoukhy
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Image texture analysis plays an important role in object detection and recognition in image processing. The texture analysis can be used for early detection of breast cancer by classifying the mammogram images into normal and abnormal classes. This study investigates breast cancer detection using texture features obtained from the grey level cooccurrence matrices (GLCM) of curvelet sub-band levels combined with texture feature obtained from the image itself. The GLCM were constructed for each sub-band of three curvelet decomposition levels. The obtained feature vector presented to the classifier to differentiate between normal and abnormal tissues. The proposed method is applied over 305 region of interest (ROI) cropped from MIAS dataset. The simple logistic classifier achieved 86.66% classification accuracy rate with sensitivity 76.53% and specificity 91.3%.

Paper Details

Date Published: 10 January 2014
PDF: 6 pages
Proc. SPIE 9069, Fifth International Conference on Graphic and Image Processing (ICGIP 2013), 906924 (10 January 2014); doi: 10.1117/12.2054183
Show Author Affiliations
Syed Jamal Safdar Gardezi, Univ. Teknologi Petronas (Malaysia)
Ibrahima Faye, Univ. Teknologi Petronas (Malaysia)
Mohamed Meselhy Eltoukhy, Univ. Teknologi Petronas (Malaysia)
Suez Canal Univ. (Egypt)

Published in SPIE Proceedings Vol. 9069:
Fifth International Conference on Graphic and Image Processing (ICGIP 2013)
Yulin Wang; Xudong Jiang; Ming Yang; David Zhang; Xie Yi, Editor(s)

© SPIE. Terms of Use
Back to Top